Inter‐annual changes in prey fields trigger different foraging tactics in a large marine predator
Bibliographic record
Abstract
We report on inter‐annual comparisons of the foraging behavior of Global Positioning System–equipped chick‐rearing northern gannets (Morus bassanus) in the western Atlantic during years with contrasting oceanographic and prey conditions. We hypothesized that the predators would modify their foraging tactics when small fishes (capelin [Mallotus villosus]) and large pelagic fishes (mackerel, saury) varied in inter‐annual abundances. We predicted differences in (1) diving behavior, (2) spatial, and (3) temporal patterning of foraging behavior. Predictions 1 and 2 were supported, prediction 3 rejected. Dives were significantly deeper (4.3 ± 0.4 vs. 2.7 ± 0.3 m) and longer (10.1 ± 1.0 vs. 5.0 ± 0.2 s), and more U‐shaped dives (dives where birds stayed at more or less one depth) were performed (52% ± 7% vs. 7% ± 2%) in the year with higher abundance of forage fishes. Flight patterns exhibited remarkable spatial and geographic differences: gannets flew significantly (17%) more and foraging ranges were about twice as long when they pursued large pelagic fishes (mean = 122 ± 81 km vs. 62 ± 12 km). The 95% kernel feeding range was 34 times larger when large pelagic fishes were available. Yet foraging trip durations were not different between years. Inter‐annual variation in foraging tactics by the same species at the same colony in successive years was strongly related to prey availability, showing that spatial foraging parameters can be determined largely by ocean and prey conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".